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Machine Learning with Go Quick Start Guide

You're reading from   Machine Learning with Go Quick Start Guide Hands-on techniques for building supervised and unsupervised machine learning workflows

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Product type Paperback
Published in May 2019
Publisher Packt
ISBN-13 9781838550356
Length 168 pages
Edition 1st Edition
Languages
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Authors (2):
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Michael Bironneau Michael Bironneau
Author Profile Icon Michael Bironneau
Michael Bironneau
Toby Coleman Toby Coleman
Author Profile Icon Toby Coleman
Toby Coleman
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Toc

Classification

When starting any supervised learning problem, the first step is to load and prepare the data. We are going to start by loading the MNIST Fashion dataset[3], a collection of small, grayscale images showing different items of clothing. Our job is to build a system that can recognize what is in each image; that is, does it contain a dress, a shoe, a coat, and so on?

First, we need to download the dataset by running the download-fashion-mnist.sh script in the code repository. Then, we will load it into Go:

import (
"fmt"
mnist "github.com/petar/GoMNIST"
"github.com/kniren/gota/dataframe"
"github.com/kniren/gota/series"
"math/rand"
"github.com/cdipaolo/goml/linear"
"github.com/cdipaolo/goml/base"
"image"
"bytes"
"math"
"github.com...
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